7 Basic Types of Machine Learning Algorithms You Must Know

I have listed down 7 types of Machine Learning algorithms which you must know. You should have thorough knowledge of these algorithms and techniques. 

• Why and where these algorithms are used? 
• What is the mathematics behind these algorithms?
• How are these algorithms implemented in Python and R? 
• How to measure the performance of these algorithms? 

Below is the list of basic types of Machine Learning algorithms:

1. Classification Algorithms

• KNN (K-Nearest Neighbors)
• SVM (Support Vector Machine)
• Naive Bayes
• Decision Trees and Random Forest

2. Regression Algorithms

• Linear Regression
• Logistic Regression

3. Clustering and Association Algorithms

• K-Means Clustering

4. Dimensionality Reduction Techniques 

• Feature Selection and Feature Extraction
• PCA (Principal Component Analysis)
• SVD (Singular Value Decomposition)
• LDA (Linear Discriminant Analysis)
• MDS (Multi-Dimension Scaling)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• ICA (Independent Component Analysis)

5. Regularization 

• Ridge Regression (L2 Regularization)
• Lasso Regression (L1 Regularization)
• Elastic-Net Regression

6. Ensemble Learning Techniques and Algorithms 

• Bagging and Boosting
• Random Forest
• AdaBoost
• Gradient Boosting Machine (GBM)
• XGBoost

7. Time Series Analysis and Sentiment Analysis

I will keep adding more algorithms and techniques to the list in future.